Pu Huangsheng, He Wei, Zhang Guanglei, Zhang Bin, Liu Fei, Zhang Yi, Luo Jianwen, Bai Jing
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China ; Department of Computer Application, School of Biomedical Engineering, Fourth Military Medical University, Xi'an710032, China.
Biomed Opt Express. 2013 Aug 29;4(10):1829-45. doi: 10.1364/BOE.4.001829. eCollection 2013.
Multispectral excitation-resolved fluorescence tomography (MEFT) uses excitation light of different wavelengths to illuminate the fluorophores and obtains the reconstruction image frame which is fluorescence yield at each corresponding wavelength. For structures containing fluorophores of different concentrations, fluorescence yields show different variation trends with the excitation spectrum. In this study, principal component analysis (PCA) is used to analyze the MEFT reconstructed image frames. By taking advantage of the different variation trends of fluorescence yields, PCA can provide a set of principal components (PCs) in which structures containing different concentrations of fluorophores are shown separately. Simulations and experiments are both performed to test the performance of the proposed algorithm. The results suggest that the location and structure of fluorophores with different concentrations can be obtained and the contrast of fluorophores can be improved further by using this algorithm.
多光谱激发分辨荧光断层扫描（MEFT）使用不同波长的激发光照射荧光团，并获得在每个相应波长处为荧光产率的重建图像帧。对于包含不同浓度荧光团的结构，荧光产率随激发光谱呈现不同的变化趋势。在本研究中，主成分分析（PCA）用于分析MEFT重建图像帧。通过利用荧光产率的不同变化趋势，PCA可以提供一组主成分（PC），其中包含不同浓度荧光团的结构被分别显示。进行了模拟和实验以测试所提算法的性能。结果表明，使用该算法可以获得不同浓度荧光团的位置和结构，并且可以进一步提高荧光团的对比度。
